{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "This simple notebook can be used to test that CmdStanPy has been successfully installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from cmdstanpy import CmdStanModel\n",
    "import arviz as az"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11:16:12 - cmdstanpy - INFO - compiling stan file /home/osvaldo/proyectos/06_Tutoriales/BDA_py_demos/demos_cmdstanpy/model.stan to exe file /home/osvaldo/proyectos/06_Tutoriales/BDA_py_demos/demos_cmdstanpy/model\n",
      "11:16:23 - cmdstanpy - INFO - compiled model executable: /home/osvaldo/proyectos/06_Tutoriales/BDA_py_demos/demos_cmdstanpy/model\n",
      "11:16:23 - cmdstanpy - INFO - CmdStan start processing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff93d6694b834b47aa3d5770c0721da9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eb0ed6861504e9b93dde2bf73414d43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 2 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c78a56784e94528a6bd401f658b469d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 3 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6098846568624e049bd62cf7310074ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 4 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                                                                                                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11:16:24 - cmdstanpy - INFO - CmdStan done processing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Write the Stan model to a file\n",
    "stan_code = \"\"\"\n",
    "parameters {\n",
    "    real theta;\n",
    "}\n",
    "model {\n",
    "    theta ~ normal(0, 1);\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "with open(\"model.stan\", \"w\") as f:\n",
    "    f.write(stan_code)\n",
    "\n",
    "# Compile the model\n",
    "model = CmdStanModel(stan_file=\"model.stan\")\n",
    "\n",
    "# Sample from the posterior\n",
    "fit = model.sample()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p06dPlY6171gmF/3fzxz7+h/kH9lDkyZNGDx4MAkJCaxdu5ZGjRqxevVqoqOjKzzv2muvZdasWXTs2JEWLVoQEBBAQkICa9asAeAf//gHL774YoXn/Pe//+W+++4jMjKSmJgYGjVqxPHjx1mxYgV5eXkMHTqURYsW4enpWTMvlIizMETEKS3YftSIfPwno9OzC4z03AKz41TLRx99ZABG69atjeTk5PLbZ8yYYQBGTEyMUVhYWOXjdR490QCMZm27GpmZmeW3v/766wZgDB069IznbNy40UhNTT3j9tWrVxv+/v6GxWIxtm7dWuG++Ph4Iz4+/oznJCcnG506dTIAY9KkSVXOLeIqdJpAxAkZhsG7S+KBkvPmgd52kxNVzxtvvAHAq6++SlhYWPnt11xzDVdccQVxcXH88MMPVTpWQUEBB5bOBMA2+HaKrP/7rfzhhx+mS5cuLF26lA0bNlR4Xvfu3WnY8MyNnPr27cvYsWMxDIPY2NgK90VHR58xwgAQFhbG448/DsCvv/5apdwirkRlQARISEjAYrEwbNgwsrOzefjhh2nevDk+Pj706NGDH3/8sfyx3333HX379sXPz4+wsDDuv/9+cnNzzzhmTk4OL730Et27d8ff3x9/f3/69evHZ599VmmG3377jXvvvZcuXboQFBzCjw8O58jHf+Xwok9IS0s74/FLlizBYrEwYcIETp48yd/+9jeaNGmCl5cXnTp1YsqUKTX2+lTHgQMH2LVrFz4+Plx22WVn3H/ttdcCVHhNz2XFihVkZWbgE9oUo2HL8u2bz/d4AHZ7SbmqznD/+TxHxFXYzA4g4kwKCgq48MILOXDgAEOGDCk/z33VVVexYMECtm3bxmOPPcbQoUMZOXIky5YtY9KkSZw4cYIvv/yy/DgpKSlcfPHFbN26lfDwcIYOHYphGKxcuZIJEyawfv16Jk2aVOFrP/roo2zZsoUuXboQ0roHRWlZeJxMYNKbr/HrovmsXr0af3//MzKnpaXRv39/srKyGDx4cHnm2267DYfDwe23317rr9vptmzZAkCnTp3KP0BP16NHD6Bksl51jtetW3eOAJ+tTOD2QdF42qzndbxt27bx7bffYrfbufjii6v0nFOnTvH6668DVFpwRFyeyacpRJzCgQMHDMAAjOHDhxtZWVnl93366afl57lDQkKMdevWld+XlJRkNG7c2AAqnGu+9NJLDcB44IEHjLy8vPLbk5OTjV69ehmAMX/+/AoZ5s2bZ6SlpRlbD6UZkY//ZET/Y66x78hJ48477zQA4/nnn6/w+NjY2PLM119/fYWvM3v2bAMwWrRoccb3OnTo0PLnVfXPp59+WuXX8u233zYA46qrrqr0/rS0NAMwGjRoUKXjPfTQQwZg3P/Ag0bvFxYbkY//ZMxYf6j8/s2bNxuA0aNHj0qfP2fOHGP8+PHGjTfeaAwaNMiwWq2Gl5eXMXXq1LN+zb179xrjx483xo0bZ4wYMcLw9/c3AOOvf/2rUVxcXKXcIq5EIwMip7Farbz33nv4+fmV33bLLbfw6KOPEhcXx9NPP02vXr3K74uIiOCmm27izTffZNmyZURHR7N582bmzZtH7969eeONN7Ba/3c2LiwsjA8//JAePXrw3nvvMWrUqPL7LrnkEgDe/bHk3PcVXSOIaRLCW2+9xZQpU/jhhx949tlnz8gcGBjIf//7X7y8vMpvGzNmDJ06dWL79u0kJCQQFRVVft+oUaMq/L0qYmKqfiVDVlYWAL6+vpXeX/baZmZmVut4Af5+jB8Qxf8t3MPHyw9wdY+mWCyWPzzeli1bKpya8fHx4e2332bcuHFn/ZrHjh0743TO/fffz7///e8K/z1F6guVAZHTREVF0aZNxe2BrVYrkZGRpKamMmLEiDOeUzbh7OjRowAsWrQIKPlAruyDo2wOwdq1a8+4b/mWvUyfNoXC1MMcTAxkwnwPoOQ89b59+yrN3LNnz0onyrVp04bt27dz9OjRCh/+TzzxRKXHcQU39W3Bf3+NY9fRDFbGn2BgTOgfPufpp5/m6aefJi8vj7i4ON577z3uvPNO5syZw8yZMyudAzBo0CAMw6C4uJiDBw8ye/Zsnn/+eebPn8+iRYuqXaZEnJ3KgMhpmjZtWuntZefqK7u/7L78/HygZDIiwFNPPcVTTz111q+Vl5dX4e9vvPEGjz72OI7iIgBmbajsWWdq1qzyxYgCAgIq5Kopy5cv5+OPPz7j9tdee43Q0NDy1yMnJ6fS52dnZ1fI90dOP16wryfX9WrG56sS+fi3/QyMCa3y8by9venUqROTJ0/Gw8ODSZMmMWnSJP7+97+f9TkeHh60bNmShx9+mKioKK655hruu+++ak1WFHEFKgMip/mjIeCqDBE7HCU77A0aNIhWrVpV6euuXr2av//971i9/Gg48j4+e2oCF/VsWz70HxERUT7ycD6ZTvfyyy+ze/fuaj3n9ttvZ9CgQQDExcVVekXEc889R2hoKC1atADg8OHKtx4uuz0yMrJKX/v3x7t1YEu+WJ1I7J7jxKVkVvt4AOPGjWPSpEn88MMP5ywDp7vqqqvw9/dnwYIFFBQU6KoCqVdUBkRqWNlv6mPGjKnyB83s2bMBCB4yjouuvI7LBnQpvy83N5fk5JrbtW/BggUsXbq0Ws8ZNmxYeRmYMGECEyZMOOtju3btCsD27dspLCw844qCjRs3AtClS5cznnuu45U9LyrUj4vbh7Fo5zE+WX4A3x3VOx5AaGjJ6YXjx49X+TkWi4UGDRpw8OBBTp06VWH9BBFXp5kwIjWs7HK1sg/4qjiakgqAR0DoGcsOf/fddzW6Y9+SJUswDKNaf8714f97LVu2pH379uTm5jJ37twz7p8xYwYAl19+eZWON3DgQIKCgoiPj2fz5s0A3DGkZJ7GzI1JfDv9u2odDygvQ1UduQHYv38/hw4dIjAwsLxMiNQXKgMiNaxv375cfPHFrFixgnvuuYeMjIwzHrNlyxYWLFhQ/vdUjwYAWPfG0i8qqPz2nTt3lq9850oefvhhAB577DFSUlLKb581axZz5swhJiaGK6+8ssJzZs+eTbt27bjlllsq3O7p6cm9994LwD333EN2dja9IkPo2iyI1JUz2bF9G0OHDqVnz57lzzl+/DgfffRRpfMWFi9ezGOPPQbAxIkTK9w3adKkSkdh9uzZw4033ohhGNxyyy14eHhU5+UQcXo6TSBSC6ZNm8aoUaN49913+eqrr+jWrRsRERGkp6ezdetWDh06xAMPPMCoUaM4lV1AfHBvPPxCOLZ9Be3ataN3796cPHmSpUuXMmbMGNauXUtiYqLZ31aV3XrrrcybN6/8A75so6KlS5fi4+PDtGnTsNkqvv2kp6ezZ88ewsPDzzje008/zc8//8zKlStp3bo1gwcPZveufZzatgmbXxDvffBRhcdnZ2dz55138uCDD9KzZ0+aNWtGdnY2e/fuLZ8v8dBDD3HNNddUeN7rr7/Ogw8+SNeuXYmJicEwDBITE9mwYQMOh4MhQ4bw0ksv1fCrJWI+jQyI1ILGjRuzcuVK3nnnHTp06MCmTZuYMWMGW7duJTo6mv/7v//jkUceAeDTFQfIt/kx+NGPuPHGGykoKGDOnDkkJSXx73//m6+//trk76b6rFYr3333Ha+//joRERH89NNPbNu2jWuuuYb169fTt2/fah3P29ub2NhYnnnmGXx9ffn+++/JOH6URj1HEnbL22xO96rw+MaNG/Pqq68ybNgwDh48yPfff8/ChQvJy8vj+uuvJzY2tnz/hNP95z//4frrryc7O5uFCxfyww8/cPDgQS6++GKmTp1KbGxspatAirg6bWEsYqKMvEIGvvwrmXlFvHdTDy7p3MTsSC7l49/288LcXUQ38uPnh4ZitVrMjiTikjQyIGKiz1cmkJlXROvG/ozseObwuJzb2N7NCfCysf94NrF7Uv74CSJSKZUBEZNk5xfxyfIDANw7PEa/1Z6HAG87N/QtWYfgw2X7TU4j4rpUBkRM8uWaRE7lFBLV0JfLdHrgvE0YEIXNamHNgZNsPZxmdhwRl6QyIGKCvMJiPlxWMipw9wUx2Dz0T/F8RQT7MLpLSZn66LcDJqcRcU16BxIxwTdrD5KalU/TYB+u6l75fghSdbcPLlmEaN62oySl5ZqcRsT1qAyI1LH8omLeX1pyfvtvw1ph16jAn9apaRADWjWk2GHw6XKNDohUl96FROrYzA1JJGfkERboxbU9K99xUKqvbInib9YdIiOv0OQ0Iq5FZUCkDuUXFTM5Ng6AO4e0wtuuZW1ryrA2jWjd2J+s/CK+XnPQ7DgiLkVlQKQOTV93iKS0XBoHeHFT6SVxUjMsFgt3lM4d+GT5AfIKi01OJOI6VAZE6kheYTGTfi0ZFbhveIxGBWrBmO5NaRLkTUpmPjM3HjY7jojLUBkQqSNfrEokJbPkCoK/9G5udpx6ydNm5c7SuQPvL42nqNhhciIR16AyIFIHsvKLeG9pPAAPXNgaL5tGBWrL9b1b0MDPk0Mnc/lx6xGz44i4BJUBkTowdcUBTmYX0DLUj6t7aF2B2uTj6cFtg1oC8G5sPA6H9mIT+SMqAyK1LD2nkA9K181/8KLWWm2wDozrH0mAl419KVks3nXM7DgiTk/vSiK17KPf9pOZV0TbsAAu7xJhdhy3EOht55YBkQBMjo1DO7WLnJvKgEgtSs3KZ8qKkhXxHrq4jXYmrEO3DmyJt93K1sPpLI9LNTuOiFNTGRCpRW/9vJecgmK6NAtiZMcws+O4lYb+XtzQp2Qth/+WXtIpIpVTGRCpJXEpmXy99hAAT17aHotFowJ17c4h0dg9SrY3XrP/hNlxRJyWyoBILXl5/m6KHQYXdwijX3RDs+O4pSZBPvylV8maDm/+vNfkNCLOS2VApBasjE/l510p2KwWnrikndlx3No9F8Tg6WFl9f6TrIzX3AGRyqgMiNQwh8PgP3N3AXBT3xa0auRvciL3FhHsw/V9SkYH3lq8T1cWiFRCZUCkhs3elMSOIxkEeNm4/8LWZscR4O5hMXjarKxNOMnKeM0dEPk9lQGRGpRbUMxri/YAcM/wGBr6e5mcSADCg7y5sfTKgjcW79XogMjvqAyI1KBPlu/naHoeTYN9mDAgyuw4cpq7h7XCy2ZlQ+IpftunuQMip1MZEKkhxzPzeW9JyWZEj41qqy2KnUzjQG9u7leyKqFGB0QqUhkQqSFv/ryX7IJiujYL0rLDTuqvQ1vhbbey+VAaS/YeNzuOiNNQGRCpAfuOZfLN2oMAPHVZBy077KQaBXhxS/8oAN7U6IBIOZUBkRrw0vzdOAwY2TGMPi0bmB1HzuHOIdH42D3YejidX3enmB1HxCmoDIj8SSviUvl1d8kCQ4+P0gJDzi7U34vxpZM73/xZowMioDIg8qcUOwxeKF1g6OZ+kURrgSGXcOeQaPw8PdielMHincfMjiNiOpUBkT9h1sbD7DqaQYC3FhhyJQ38PE8bHdiHw6HRAXFvKgMi5+n0BYbuGx5DAz9PkxNJddwxOBp/Lxu7jmawaGey2XFETKUyIHKePvptP8cy8mkW4lM+Q11cR4ifJxMHRgHwlkYHxM2pDIich5TMPN5fWrLA0OOj2mmBIRd1+6BoArxs7E7OZP52jQ6I+1IZEDkPby7eS05BMd2aBzO6SxOz48h5CvK1c+uglgC89fNeijU6IG5KZUCkmvYkZ/LtukMAPH1ZeywWLTDkym4d1JJAbxv7UrKYu+2o2XFETKEyIFJNL83fhcOASzqF0ytKCwy5uiAfO7cPjgbgnV80d0Dck8qASDX8tu84S/Ycx+6hBYbqk4kDowj0thGXkqW5A+KWVAZEqqjYYfCf0gWGxvWLIirUz+REUlMCvP83d2DSrxodEPejMiBSRTM3HmZ3ciaB3jbuvzDG7DhSwyYOaIl/6ZUFi7QqobgZlQGRKsgrLObNxXsBuHd4DMG+WmCovgnytTOhdFXCSb/u054F4lZUBkSq4PNVCRxNzyMiyFsLDNVjtw1qia+nBzuOZGhHQ3ErKgMifyA9t5DJsSULDD14cRstMFSPhfh5Mq5/JADv/Bqn0QFxGyoDIn/gg6XxpOcW0ibMn2t6NDM7jtSyOwZH4223suVQGsv2pZodR6ROqAyInMOxjDymrDgAwKMj2+Fh1QJD9V2ovxc39S0dHfhFcwfEPagMiJzDWz/vI6/QQa/IEC5q39jsOFJH7hoSjafNyobEU6yKP2F2HJFapzIgchbxx7OYvr5k2eHHL2mnZYfdSONAb27s0wKAt3/ZZ3IakdqnMiByFm8sKtm45sJ2jemtZYfdzl1Do/H0sLLmwEk2JJ40O45IrVIZEKnErqMZzN12FIsFHh3V1uw4YoImQT5c1b0pAO8v3W9yGpHapTIgUol3SoeGL+vchHbhgSanEbPcOTQaiwUW7zxGXEqm2XFEao3KgMjv7DqawfztyVgscP+Frc2OIyZq1cifER3CAPhAowNSj6kMiPzO6aMCbcICTE4jZrtraCsAvt+cxNH0XJPTiNQOlQGR02hUQH6vR4sQ+rRsQGGxwZTlB8yOI1IrVAZETqNRAanM30pHB75ac5D03EKT04jUPJUBkVIaFZCzGda2EW3DAsguKGba6kSz44jUOJUBkVLvLSnZjOhSjQrI71gsFu4aGg3ApysSyCssNjmRSM1SGRABDp7I4aetR4D/DQmLnO7yrhE0DfYhNSufWRuTzI4jUqNUBkSAj37bj8OAwa1D6dQ0yOw44oTsHlZuHdQSgE+W78fh0AZGUn+oDIjbS83KL9+D4G/DNCogZ/eXXs3w97IRfzybpfuOmx1HpMaoDIjb+2xlAvlFDro2C6J/dEOz44gTC/C2M7Z3cwBdZij1isqAuLWcgiI+X1UyO/yvQ1tpZ0L5QxMGRGG1wG/7UtmdnGF2HJEaoTIgbm3mxiTScwuJbOjLiI7hZscRF9C8gS+jOpX8rGh0QOoLlQFxWw6HwdQVJW/mEwZE4WHVqIBUzW2lEwm/33yE1Kx8k9OI/HkqA+K2lu07TvzxbPy9bFzbs5nZccSF9GgRQrfmwRQUObQIkdQLKgPitj5dkQDAdb2aEeBtNzeMuBSLxVI+OvDFqkQtQiQuT2VA3FJcShZL9x7HYik5RSBSXZd0CiciyJsT2QXM2XzE7Dgif4rKgLilz1YmAHBhuzAiG/qZG0Zcks3DyoSBUQB8svwAhqFFiMR1qQyI20nPKWTGhsMA3Fr6Zi5yPsb2boGvpwd7jmWyPC7V7Dgi501lQNzO9PWHyC0spl14AP1baZEhOX9BPnb+0qtkEaJPdJmhuDCVAXErhmHw1dqDAIwfEKVFhuRPmzgwCosFluw5TlxKptlxRM6LyoC4lVX7T3AgteRywiu6RpgdR+qByIZ+XNw+DIAppVeoiLgalQFxK1+tKRkVuLJbBH5eNpPTSH1Rtpvh7I1JpOcUmpxGpPpUBsRtnMjKZ+GOZABu6NPC5DRSn/Rt2YB24QHkFhbz3YZDZscRqTaVAXEbMzYcprDYoGuzIDo1DTI7jtQjFouF8aXrVXy+KpFihy4zFNeiMiBuwTAMvi6dOHhjX40KSM0b060pQT52Dp7MIXZ3itlxRKpFZUDcwqr4EyScyMHfy8boLpo4KDXPx9ODsb1LLjP8bFWCuWFEqkllQNxC2eWEY7pr4qDUnnH9IrFY4Ld9qcSlZJkdR6TKVAak3kvVxEGpI80b+HJhu5LLDD/X6IC4EJUBqffKJw42D6ZjhCYOSu0q2/hq5obDZObpMkNxDSoDUq8ZhsE3pacIbtKogNSBgTENiWnsT3ZBcfkeGCLOTmVA6rUNiadIOJGDn6cHo7s2MTuOuAGLxcL4/pFAyWWGDl1mKC5AZUDqtZkbS34zu6RzE3w9NXFQ6sbVPZoR4GXjQGo2y/YdNzuOyB9SGZB6K6+wmJ+2HgXgmh7NTE4j7sTPy8a1vUp+5j5bmWBuGJEqUBmQemvxzmNk5hXRNNiHvi0bmB1H3Mz4/lEALNl7nIMncswNI/IHVAak3ppVeorg6h5NsVq1VbHUrahQPwa3DsUw4Mu1iWbHETknlQGpl1Iy81i2LxWAq7o3NTmNuKub+5VMJPxu/WHyi4pNTiNydioDUi/N2XyEYodBjxbBRDfyNzuOuKkL2zWmSZA3J7MLWLA92ew4ImelMiD10syNSUDJrG4Rs9g8rFzfu2R9i2mrdapAnJfKgNQ7O49ksOtoBp4eVi7XpkRisuv7NMfDamFdwil2J2eYHUekUioDUu+UTRy8qENjgnztJqcRdxcW6M2IDiX7FXy5+qDJaUQqpzIg9UpRsYPvNx8B4OruOkUgzuGmviUTCWdvSiI7v8jkNCJnUhmQeuW3famkZuXT0M+ToW0bmR1HBIABrRrSMtSPrPwivt+cZHYckTOoDEi9MqP0FMEV3SKwe+jHW5yD1Wrhpr5lEwkPYhjar0Cci94tpd5Izy1k8c5jgJYfFudzbc9meNms7DqawcaDaWbHEalAZUDqjblbj1JQ5KBtWAAdIwLNjiNSQbCvJ6NLr275UpcZipNRGZB6o+wqgmt6NsVi0fLD4nxu7ldyquCnbUc5lV1gchqR/1EZkHohITWb9YmnsFpgTDctPyzOqVvzYDpGBFJQ5GDGhsNmxxEppzIg9cKsTSUztAe3bkTjQG+T04hUzmKxlF9m+OWaRBwOTSQU56AyIC7P4TAq7FAo4syu7BZBgJeNhBM5rNp/wuw4IoDKgNQD6xJOcvhULgFeNkZ2DDc7jsg5+XnZuKJbyUTCb9YdMjmNSAmVAXF5M0tHBS7t3ARvu4fJaUT+WNnmRQu3J2sioTgFlQFxabkFxczbVrI1rE4RiKvo3CyoZCJhsYPZm7QioZhPZUBc2qKdyWTlF9G8gQ+9oxqYHUekyq7v3RyAb9ZpRUIxn8qAuLSZG0t+q7qqezOsVq0tIK7jim5N8bJZ2Xssi02H0syOI25OZUBc1rGMPJbvOw7ANTpFIC4myMfOZZ2bAPDtWk0kFHOpDIjL+n5TEg4DekWGENnQz+w4ItV2fZ+SiYQ/bj1ClrY2FhOpDIhLMgyj/CqCa3pqUyJxTb2jQogO9SOnoJgftxwxO464MZUBcUk7jmSw91gWnjYrl5YOtYq4GovFwtjyiYQ6VSDmURkQl1S2rvuIDmEE+dhNTiNy/q7u0Qyb1cKWQ2nsOpphdhxxUyoD4nIKihzMKR1SvaaHThGIa2sU4MXFHcIA+FajA2ISlQFxOUv2pHAyu4BGAV4Mbh1qdhyRP+26XiWlds6WIxQUOUxOI+5IZUBcTtnEwau6N8XmoR9hcX1DWjci1N+Lk9kFLNmTYnYccUN6JxWXcjK7gF93l7xZ6hSB1Bc2DytXdS/ZvKhsPoxIXVIZEJcyZ3MShcUGnZoG0jY8wOw4IjWm7BLZ2NLTYCJ1SWVAXErZ8sMaFZD6pl14IJ2aBlJYbDBnszYvkrqlMiAuY09yJtuS0rF7WLiym5YflvqnrOSWlV6RuqIyIC6jbOLgBW0b08DP0+Q0IjXviq4R2KwWtiWlsyc50+w44kZUBsQlFJ2277uWH5b6qqG/Fxe0awz8r/yK1AWVAXEJv8WlcjwznxBfOxe0bWx2HJFac21p2Z29KYmiYq05IHVDZUBcQtnlVld2a4qnTT+2Un9d0LYxIb52jmfm89u+VLPjiJvQu6o4vfScQhbvPAb877cmkfrK02YtnyA7Q6cKpI6oDIjT+2lbyRKtbcMC6BgRaHYckVpXVnoX7zxGRl6hyWnEHagMiNObWXqK4JqeTbFYLCanEal9HSMCiWnsT0GRg4Xbk82OI25AZUCcWvzxLDYeTMNqgTFaW0DchMVi4cquJcsTl+3QKVKbVAbEqc0qPWc6pE0jGgd6m5xGpO5c0a2kDKyISyUlM8/kNFLfqQyI0yp2GMzcoOWHxT1FNvSjW/NgHAbM3XrU7DhSz6kMiNNatvc4yRl5BPvaGdExzOw4InXuytLRge8361SB1C6VAXFa36w7CMDV3ZvhZfMwOY1I3RvdJQKrBbYcSiMhNdvsOFKPqQyIU0rJzOOXXSkAjO3d3OQ0IuZoFODFwJhQQBMJpXapDIhTmrUxiSKHQfcWwbQNDzA7johpyhYg+n5zEoZhmJxG6iuVAXE6hmEwfd0hAMb20qiAuLeRHcPwtFnZfzybHUcyzI4j9ZTKgDiddQmn2J+aja+nB6NLr7UWcVcB3nYual+yOZdOFUhtURkQp1M2cfDyLhH4e9lMTiNiviu6lpwqmLP5CA6HThVIzVMZEKeSkVfIvG0l11SP7aNTBCIAw9o2IsDbRnJGHmsOnDQ7jtRDKgPiVOZsPkJeoYM2Yf50bx5sdhwRp+Bt92BUx3AA5m7TqQKpeSoD4lS+LZs42LuFNiUSOU3Z/JkF25Mp1qkCqWEqA+I0tielsy0pHbuHhau6a1MikdMNaNWQYF87qVkFrDlwwuw4Us+oDIjTmL6+ZFRgRMdwGvh5mpxGxLnYPayM7FB6qkB7FUgNUxkQp5BbUMz3m0o2JbpeKw6KVOqyLk2AklMFRcUOk9NIfaIyIE7hx61HyMgronkDHwa2CjU7johT6t+qISG+dk5kF+iqAqlRKgPiFL5cnQjAjX0isVo1cVCkMnYPKyPLryrQqQKpOSoDYrqth9PYcjgdTw8rf+nVzOw4Ik5NpwqkNqgMiOmmlY4KXNo5nIb+XianEXFu/aNLThWczC5g9X6dKpCaoTIgpkrPKSxfb/3mfpEmpxFxfjYPK6M6lYwOaAEiqSkqA2KqmRsPk1fooF14AD0jQ8yOI+ISLuusUwVSs1QGxDSGYfDlmpJTBDf1i9SKgyJV1C+6AQ38PDmVU8iq/VqASP48lQExzar9J4g/no2fp4dWHBSphpJTBVqASGqOyoCY5svVJVsVj+neVFsVi1TT6LJTBTuSKdSpAvmTVAbEFCkZeSzckQxo4qDI+ejTsgGh/p6k5RSyKl6nCuTPURkQU3y99hBFDoOekSG0bxJodhwRl2M7fQEinSqQP0llQOpcQZGDaaUTB8cPiDI3jIgLK1+ASKcK5E9SGZA6N2/bUY5n5hMW6MUlpZOgRKT6+rZsSKi/J+m5hayISzU7jrgwlQGpU4Zh8OmKAwCM6xeJ3UM/giLny8Nq0VUFUiP0Tix1auPB0n0IbFZu6NPC7DgiLu/S0qsKFu86plMFct5UBqROTV2ZAMCVXSO0D4FIDSg7VaCrCuTPUBmQOpOcnsf80m1XJw5saXIakfrBw2opv6pgnrY1lvOkMiB15ovVCRQ5DPq2bECHCF1OKFJTyk4VLNRVBXKeVAakTuQVFvPVmpIVBycOjDI3jEg907fl//YqWKNtjeU8qAxInZiz+QincgppGuzDxR10OaFITSpZgCgMgLk6VSDnQWVAap1hGEwpvZxw/IBIPKz1e3fCXbt2cdNNN9GkSRO8vLyIiori3nvvJTW16teB33bbbVgsFiwWC8uXLz/jfofDwbPPPktERAQ+Pj4MGzaMrVu3VnqsoqIiOnfuzIABAzAMo9rfT1mOc5k6dSoWi4UJEyZUevvpf/z8/IiIiGDYsGE8/vjj7Nixo9rHlTOdfqpA2xpLdakMSK1bc+Aku5Mz8bF7MLZX/b6c8Ndff6VXr1589dVXBAcHM3r0aLy8vJg8eTLdu3fn8OHDf3iM2NhYpkyZcs4P4FdeeYV///vfBAUFcfHFF7Nq1SouuugiMjMzz3jspEmT2LlzJ5MnTzZtm+hWrVoxfvx4xo8fz5VXXkmnTp3YsWMHr776Kp06deLmm28mIyPDlGz1Rb/ohoT42jmZXcDaAzpVINWjMiC1bsryklGBq3s0JcjXbnKa2pOTk8ONN95ITk4Ozz77LLt27WLmzJns3r2bRx55hMOHD3Pbbbed8xh5eXncdddddOzYkf79+1f6mMLCQl599VW6du3K5s2bmTNnDlOmTOH48eN88MEHFR577NgxnnvuOe666y66d+9eY99rdQ0aNIipU6cydepUvvrqKxYtWkRKSgo//vgjUVFRfPnll1xxxRUUFhaaltHV2T2sjCg9BadTBVJdKgNSq/Yfz2LxrmNA/Z84OGvWLI4dO0bbtm355z//WX67xWLhxRdfJCoqikWLFrFly5azHuPf//43cXFxvP/++9jtlRenhIQE0tLSuP766/HyKlmr4YYbbsDb25vNmzdXeOxjjz2G3W7nhRde+PPfYA2zWCyMHj2aNWvWEBERwdKlS3nvvffMjuXSLu3yv1MFxY7qnxIS96UyILXq4+UHMAy4qH1jYhoHmB2nVm3YsAGAIUOGYLVW/Kdlt9sZOHAgAD/88EOlz9+2bRv/93//x6233sqgQYPO+nVOnToFQEhISPltVquVoKCg8vsAVq5cyRdffMFLL71EgwYNzu+bqgONGzfmX//6FwDvvPOOyWlc24BWDQnysZOapVMFUj0qA1JrUrPymbGh5Bz5HYOjTU5T+7Kzs4GKH9Kna9iwIUClIwMOh4M777yT4OBgXn311XN+nRYtSuZd7N27t/y2U6dOcfz48fL7HA4H9957Lz179vzDUxPO4C9/+QtWq5X4+PgqzauQypWcKii5qkALEEl1qAxIrfl8VSIFRQ66Ng+mT0vn/c20pjRq1AiAxMTESu8/cODAWe+fPHkyq1ev5rXXXvvD3+LDw8Pp0aMHn376KcuXL+fUqVM8/PDDOBwOLrvsMgDef/99Nm/ezOTJk88YpXBGAQEBREeXFMadO3eanMa1XXratsY6VSBV5fzvEuKScguK+WJVAgB3Do42bRZ7XRoyZAgAc+fOPeMywqSkJBYvXgxwxoz/w4cP89RTTzFs2DBuueWWKn2t119/nezsbAYPHkyDBg2YOnUql156KaNHj+bEiRM888wz3HrrrfTp06f8OXl5eTgc53/J2e8vETz9z8SJE8/7uGVCQ0MBKpzqkOob2CqUQG8bxzPzWZ+gUwVSNTazA0j9NGPDIU7lFNK8gU/5Fqv13YgRI+jRowcbN27kkksuYfLkyXTo0IFt27Zx1113UVRUBHDGb+r33HMP+fn51Zo8N2zYMDZu3MgXX3xBWloaffv2Zdy4cQD84x//wDAMXn75ZQB++eUX7r//fnbu3ImPjw/jxo3j7bffxtvbu1rf3/jx4896X1xcHCtWrKjW8X6vbA0EdyiOtcnTZuXiDuHM3HiY+duT6Rvd0OxI4gJUBqTGFTsMPi69nPD2QdH1fpGhMhaLhVmzZnHZZZexfv16+vbtW35fWFgYzz33HE8//XSFOQUzZ85kzpw5PPPMM7Rr165aX69jx47lH/hl1q9fzyeffMI777xDaGgoSUlJXH755XTq1ImZM2eyc+dOnnvuOfz8/HjjjTeq9fWmTp16zvv+bBkoG01x5smOruKyLmVl4CjPju6A1U3+Dcr5UxmQGrdwRzKJJ3II9rVzXa9mZsepU5GRkWzevJnZs2ezcuVKcnNz6dixIzfddBOzZs0CSj7Ey/z4448ALF68mGXLllU4Vtllgvfddx9BQUFMmDDhnCvxGYbBPffcQ5cuXfjrX/8KlMxFyMvLY/r06URFRXH11VcTFxfH5MmTeeGFF/D19a3B7/78ZWRksH//fgA6dOhgchrXNzAmlAAvG8cy8tlw8BS9o1Sw5NxUBqRGGYbBB8tK3tTH9YvE19P9fsRsNhvXXXcd1113XYXbV65cCZQM8f/e6tWrz3q8slJQ2fNON2XKFNatW8dvv/2Gh4cHALt37yY0NJSoqKjyx/Xp04fPPvuMuLg4unTp8sffUB2YPn06hmHQpk0bIiIizI7j8rxsHlzcIYxZm5KYt+2oyoD8IU0glBq15sBJthxKw9Nm5Zb+UWbHcRrJycnMmDGDhg0bcvXVV5ffPnXqVAzDqPTP0KFDAfjtt98wDIPnnnvurMdPS0vjH//4B+PGjStfz6BMbm5uhb+XXQLpLFcZpKSk8OyzzwLwwAMPmJym/rikdK+C+duSceiqAvkDzvFuIPXGu0viAbiuZzMaBXiZnKbubd++nby8vAq3HT58mCuvvJLMzExef/11fHx8avzrPv300+Tn55+xRkHHjh3JysoqX+iosLCQ7777Di8vL1q1alXjOarDMAzmzZtH3759OXr0KMOHD+fOO+80NVN9Mrh1KP5eNpIz8th0SFdoyLm53xiu1Jpth9NZtvc4HlYLdw0x94PGLK+99hqzZ8+mR48eNGnShJSUFJYvX05+fj7PPPPMOWfkn68tW7bw/vvv89prrxEWFlbhvnvuuYe33nqLsWPHMnLkSOLi4ti5cydPPPFErZSSs1m+fHn5fIeCggJOnDjBxo0byycNjhs3jsmTJ2Oz6S2ppnjbPbiofWO+33yEuVuT6RmpUwVydvqXJzXmvaVxAFzepQktGjrHxLS6NmbMGJKTk9myZQsrVqwgJCSEUaNG8eCDD/7hOf/zdd9999G+fXvuvffeM+4LDw9n4cKFPPLIIyxYsIDg4GAeeeSR8uV/60p8fDzx8SWjRj4+PgQHB9OhQwf69evHLbfcUmFSpdScy7pE8P3mI/y09QhPXdbeba7skeqzGOezwbnI78SlZHHxm0sxDFj44BDahtfvfQhEXEFBkYPe//mZ9NxCvrq9LwNiQs2OJE5KcwakRnywNL50Q6IwFQERJ+Fps3Jp6UTCHzYfMTmNODOVAfnTktJymb0pCYC7L3DPuQIizurKbiWXas7bfpT8omKT04izUhmQP+2jZfspchj0j25IjxaV79gnIuboE9WA8EBvMvOKiN193Ow44qRUBuRPOZGVzzfrDgJwzwUxJqcRkd+zWi1cUTo6MGdLkslpxFmpDMifMmXFAfIKHXRpFsTAGG2IIuKMruhaUgZ+3pVCZl6hyWnEGakMyHlLzynks5WJANw9LEa7zYk4qY4RgbRq5EdBkYOFO46ZHUeckMqAnLcpKw6QlV9Eu/AARnQI++MniIgpLBYLY7o1BeCHzTpVIGdSGZDzkpFXyJQVJdsU3ze8tbZIFXFyZfMGVsSlcjwz3+Q04mxUBuS8fL4ygcy8Ilo39ueSTuFmxxGRPxDZ0I9uzYNxGDB3q9YckIpUBqTasvKL+Hh5yajAvcNjNCog4iLK1hz4XgsQye+oDEi1fbEqkbScQqJD/RjdRXvPi7iKy7o0wWqBzYfSiD+eZXYccSIqA1ItOQVFfPzbfqBkXQFtfCLiOhoHeDOsbWMAZmw4bHIacSYqA1ItX605yInsAlo08C0fchQR13Fdz2YAzNp4mKJih8lpxFmoDEiV5RUW8/7SklGBey+IweahHx8RV3Nh+zAa+HlyLCOf3/almh1HnITezaXKvl57kNSsfJoG+3BVj6ZmxxGR8+Bps5aP6n234ZDJacRZqAxIlZSMCsQDJTsT2jUqIOKyruvZHIDFO49xMrvA5DTiDPSOLlXy3YbDHMvIp0mQN9eWnnMUEdfUISKQTk0DKSw2tCKhACoDUgUFRQ7ei40D4G/DWuFl8zA5kYj8WX/pVTI68PXagxiGYXIaMZvKgPyhmRsPcyQ9j8YBXuVvICLi2q7s1hQfuwd7j2WxLuGU2XHEZCoDck6FxQ7eXVIyKnDX0FZ42zUqIFIfBPnYyycSTludaHIaMZvKgJzT95uSOHQyl1B/T27s08LsOCJSg27uFwnA/O1HSc3S5kXuTGVAzqqo2MHk0rkCdw6JxsdTowIi9UmnpkF0bR5MYbHBd+u1IqE7UxmQs/pp61ESTuQQ4mvnpr6RZscRkVpwc9+SEb+v1ibicGgiobtSGZBKFTsMJv26D4DbB0fj52UzOZGI1IbLu0YQ5GPn0MlcluxNMTuOmERlQCo1b9tR4o9nE+Rj55b+GhUQqa+87R6M7V1yldAnpVuTi/tRGZAzOE4bFbh1YEsCvO0mJxKR2jR+QBQeVgsr4k6w80iG2XHEBCoDcoZFO5PZeyyLAC8bEwZGmR1HRGpZ02AfLu3cBICPl+83OY2YQWVAKjAMg7d/KbmCYOLAKIJ8NCog4g5uG9QSgB+3HOFYRp7JaaSuqQxIBT/vSmHX0Qz8PD24tfTNQUTqv27Ng+kdFUJhscHnqxLMjiN1TGVAyhmGwTu/lMwVuGVAFMG+niYnEpG6dNugaACmrT5IVn6RyWmkLqkMSLkle4+zLSkdH7sHt2tUQMTtXNwhjOhQP9JzC/lSSxS7FZUBAUrnCvxcMiowrn8kDf29TE4kInXNw2rhr8NaAfDRbwfIKyw2OZHUFZUBAWB5XCqbD6XhZbNyx+Bos+OIiEmu6t6UpsE+pGbl8+26Q2bHkTqiMiAV5grc2LcFjQI0KiDiruwe1vLRgQ+WxlNQ5DA5kdQFlQFh9f6TrEs4hafNyl+HtjI7joiY7LqezWgc4MWR9DxmbdQGRu5AZUDKRwWu792csEBvk9OIiNm87R7cOaTkdOGkX+PIL9LcgfpOZcDNrUs4yar9J7B7WDQqICLlbu4XSVigF0lpuXy15qDZcaSWqQy4ubJRgWt7Nici2MfkNCLiLLztHtx/YWsAJsfGka11B+o1lQE3tvHgKX7bl4rNauHuYRoVEJGK/tKrOVENfUnNKuDTFdrRsD5TGXBjby7eC8DVPZrSvIGvyWlExNnYPaw8dHEbAD5Yup9T2QUmJ5LaojLgptYlnCwfFbhveGuz44iIk7q8SwTtwgPIzC/i7dLTilL/qAy4qbJRget6NdeogIicldVq4enLOgAwbXUi8cezTE4ktUFlwA2tij/ByviSKwjuHR5jdhwRcXKDWodyYbvGFDkMXpy7y+w4UgtUBtyMYRi8+XPJqMD1vVvQVFcQiEgVPHlZe2xWC7/sTmH5vlSz40gNUxlwMyvjT7D2wEk8bVbuvkBXEIhI1bRq5M/N/SIB+NdPOygs1jLF9YnKgBsxDIM3SucK3NinBU2CNCogIlX34EWtCfG1s/dYFp8s16WG9YnKgBtZti+VDYmn8LJZta6AiFRbsK8nT17aHoC3ft7LoZM5JieSmqIy4CZOHxUY1y+SxtqDQETOw7U9m9G3ZQPyCh38c84ODMMwO5LUAJUBNxG7J4Uth9LwsXtwl/YgEJHzZLFY+M9VnbF7WPh1dwoLtiebHUlqgMqAGzh9VOCWAZE0CvAyOZGIuLKYxv78rfSXiud+3EFmXqHJieTPUhlwA4t2HmN7UgZ+nh7cNUSjAiLy5919QQxRDX05lpHPS/N3mx1H/iSVgXquqNjBqwtK/qFOGBhFAz9PkxOJSH3gbffgpau7APDVmoNae8DFqQzUc99tOEz88WxCfO2aKyAiNap/q4bc0r9k7YHHZ27V6QIXpjJQj+UUFJXvQXDv8NYEettNTiQi9c3jo9rRooEvSWm5vDhPSxW7KpWBemzK8gOkZObTLMSHm/u1MDuOiNRDfl42Xr225HTB12sPsWzvcZMTyflQGainTmTl8/7S/QA8OrItXjYPkxOJSH3VL7ohEwZEAfDYjK2k5RSYG0iqTWWgnpr0axxZ+UV0ahrI5V0izI4jIvXcY6Pa0jLUj+SMPJ6cvU2LEbkYlYF6KPFENl+uSQTgH5e0x2q1mJxIROo7X08bb1/fDZvVwrxtyXy3/rDZkaQaVAbqoVcX7qGw2GBIm0YMjAk1O46IuIkuzYL5+4i2QMliRAdSs01OJFWlMlDPrNl/grlbj2KxwBOj2pkdR0TczF1Doukf3ZCcgmIe+GYTBUXa6tgVqAzUI0XFJRuHAFzfuwUdIgJNTiQi7sZqtfDG2K4E+djZejid/1uo1QldgcpAPfL12oPsTs4kyMfOoyPbmh1HRNxUkyCf8ssNP/rtgDYzcgEqA/XEyewCXltUssDQ30e00bLDImKqkR3DuWNwSwAe/W4LCZo/4NRUBuqJl+btIj23kHbhAdzYRwsMiYj5HhvVjl6RIWTmF/G3LzeSV1hsdiQ5C5UBF7J7925eeeUVLrjgAkJDQ7Hb7YSHhzNkxGV88f0CLBZ4YUwnbB76zyoiNae4uJjp06fzyCOPMGTIEPz8/LBYLEyYMOGcz7N7WPnvjT1o6OfJrqMZPPvDdgzDYNiwYVgslnP+sVr1PlaXbGYHkKq76KKLSEpKwt/fn379+tGgQQO279jBb4vnAfMZedtj9Iq6zOyYIlLPZGZmMnbs2PN6bniQN+/c0J1xn6xh+vrDdIwIYtSoUURFRVX6+A0bNrB9+3YGDx78JxJLdakMuJB27drx0ksvcd111+Ht7Q3AP3/Yzn/fe5+TCyfz89TX2PnwLXTo0MHkpCJSn9jtdsaNG0evXr3o3bs3e/bsYeLEiVV+/sCYUJ64pB0vztvNv37ayWcTb+eJ1pWvgdK3b18Axo0bVyPZpWo0DuNCfv75Z8aNG1deBNYlnOSzVYkEdLuE3oOGUVxczHfffWdyShGpb/z8/Pj888+5//776d+/f/l7UHXcMTiaq3s0pdhhcPeXGypdkGjfvn2sXbsWb29vrrvuupqILlWkMuCicgqKeHzGVgD+0qsZw/r3BuDIkSNmxhIRqZTFYuHFqzrTo0UwGXlF3PbZOtJzCys8Ztq0aQBcfvnlBAUFmRHTbakMuKjn5uxgf2o2YYFePHVZB/bvL9mhMDw83ORkIiKV87Z78P64nkQEebP/eDb3fb2JouL/rVD45ZdfAnDzzTebFdFtqQy4oB82JzF9/WEsFnhzbDdSjxzkp59+AuCKK64wOZ2IyNk1DvDmw1t64WP3YNne4zzzww4Mw2DVqlXEx8fTsGFDLrnkErNjuh1NIHQxCanZPDlrGwD3DW9Nn8hgLrjgKvLz8xk7diw9e/Y0OaGIyLl1ahrEm2O7cfeXG/h67UEa+nmy/4cvALj++uux2+0mJ3Q/GhlwIdn5Rfx12gayC4rpE9WA+4fHcP/997N8+XKio6N59913zY4oIlIlozqF85+rOgMw6efdfPHVN4CuIjCLRgacSGULeIwZM4YxY8bgcBg8PH0zu5MzCfX34u0buvHKyy/x3nvvERYWxsKFC2nQoEHdhxYROU839GnByewCnv/v52Sln6JJi5bllxZK3VIZcCKfffbZGbdFRUUxZswYXlm4m4U7juHpYeWDcT354evPePrppwkKCmLBggXExMSYkFhE5M+5e1gr3n1mLceBvMgB/Lr7GMPbhZkdy+2oDDgRwzAqvf3DZfF8sLTkaoEXr+7MvlULueeee/D19WXu3Ll069atDlOKiNSczMxM9m9YCoBPhwv46xcbeX9cDxWCOqY5A05uxobDvDivZD/wJy5ph++xrdxyyy3YbDZmz57NwIEDTU4oInL+ZsyYQV5eHgMGDGT0wK4UFDu464sNLNqhbY/rksqAE5u18TCPzyxZWOiOwS3p5HGUa6+9FsMw+PbbbxkxYoTJCUVEzu7CCy+kXbt2rF279qyPKVto6JZbxvHfG3twWZcmFBYb3P3lRuZtO1pXUd2exTjb2LSYatrqRJ7+fjtQssLgy1d3oWHDBqSlpdGyZUuGDBlS6fMGDRrE7bffXpdRRcQN3H333WzcuBGAEydOEBcXR2hoKK1atSp/zOrVqys8JyoqisTERGJjYxk2bNgZxzx8+DCRkZHYbDaSk5MJCQmhqNjBI99t4fvNR/CwWnj56s5c16t5rX5vojkDTscwDN5bGs+rC/YAML5/JP+8vCNWq4W0tDQADhw4wIEDB856DJUBEalpO3fuZM2aNRVuS01NJTU19byP+dVXX+FwOLjssssICQkBwOZh5fW/dMPuYeW7DYd5dMZWjqbncd/wGCwWy5/6HuTsNDLgRPIKi3ly1jZmbUoCSmbZPjqyrf4BiIjbMQyD/1u4h3eXxANwfe/m/OvKTnjadHa7NqgMOIljGXn8ddoGNh1Mw8Nq4dnRHRg/IMrsWCIipvpidSL//GE7DgN6R4Xw7k09aRTgZXasekdlwAn8susYj3y3hVM5hQR623j3pp4MOste3yIi7iZ2dwr3f72JzPwimgR58+5NPejeIsTsWPWKyoCJ0nIK+PdPu5i58TAAHSMC+e+NPWgZ6mdyMhER5xJ/PIs7Pl/P/uPZeFgtPHhha+6+IAYPq06j1gSVAZMs2H6Up7/fQWpWPhYL3DqwJY+NaouXzcPsaCIiTikjr5B/zNrG3K0llxz2igzhzbHdaN7A1+Rkrk9loI7FpWTx8vxd/LwrBYBWjfx49dou9IzUvgIiIn/EMAxmb0ri2R92kJVfhJ+nBw9d3IbxA6Kwe2hy4flSGagjJ7LyefuXfXy55iDFDgMPq4W/Do3mvuGt8bZrNEBEpDoOnczh79O3sDbhJABtwvx5ZnQHBsWE6gqs86AyUMtyCor4bGUi78bGkZlfBMBF7RvzxCXtiWnsb3I6ERHX5XAYzNhwmJcX7OZkdgFQcsXBgxe1YUCrhioF1aAyUEuy84v4YnUiHy3bz4nSH9KOEYE8dVl7BrTSlQIiIjUlLaeAd36J48s1ieQXOQDoGRnCxIFRjOgQrrUJqkBloIZl5xfx+apEPvptf3lTjWzoy/3DW3NV96ZYNfNVRKRWpGTk8d7SeL5ac7C8FIT6e3JV96Zc0rkJ3ZoF6z34LFQGakh6biHTVify8W/7OZVTCEBUQ1/uHd6aMd0isGlii4hInUjJyGPamoN8s/YgKZn55bc3CfJmZMdwLmjXmD5RDfDx1HytMioDf9KhkzlMWXGA6esOkV1QDEDLUD/uvSCGK1UCRERMU1js4NfdKfy09Si/7jpW/h4N4OlhpVdUCANaNaRb8xC6NA8i0NtuYlpzqQycp82H0vjot/3M33YUR+kr2DYsgLuGRnNFV5UAERFnkldYzG/7Ulm0I5nlcakcTc+rcL/FAjGN/OnWPJiuzYPpGBFIu/BAtxk9UBmohuz8In7aeoSv1h5iy6G08tsHtw7l9sHRDGmtS1pERJydYRjsT81m+b5U1ieeYvOhUxw6mXvG46wWiAr1o0OTQDpEBNK+SSAdmwTSKMCr3r3Xqwz8AcMw2J6UwbfrD/L9piNklV4eaPewcEXXptw+uCXtmwSanLJ6jh8/bnYEEXEijRo1MjuC6VKz8tl8MI3Nh9LYcjiNXUczSc3Kr/SxDf08adXYn5jG/sQ08i///xFB3i5bElQGziIuJZM5W47y05Yj7E/NLr89qqEvN/RpwTU9mxHq75o7Z7nqD6uI1A59DFQuJTOPXUcz2Xkkg11HM9h5NIP9x7PKTw3/nq+nB02DfWga4kNEsA9Ng31oFOBFiK8nIb52gkv/N9DHjs1qcar3YpWBUkXFDjYdSmPpnuP8vOsYu5Mzy+/zslm5uEMYN/ZpQb/ohi5/aYoz/QCKiPn0MVB1uQXFxKVkEXc8s+R/S/8knsih6GwtoRJWC3jZPPC0WfGyWfG0WZk4sCW3DWpZi+nPzmbKV3UC2flFbD2czsaDp9h08BRrDpwkM6+o/H6b1cKQNo24omsEF3UIw9/LbV8qEREp5ePpQedmQXRuFlTh9sJiBwdP5nAkLZekU7kcScvlcFouJ7IKSMsp4FROIadyCso/ZxwG5BYWk1v4vysccvKLMIupIwM7jqSz80gGdg8rNg8LNqsVu4cFm4cVu9WC3WbF7mHF08OKp82Cp4cHdpul5DZbye12D+sZW1g6HAa5hcVk5RdxLCOPYxn5JGfkcfBENvHHs4lLyeLQqRx+/52H+NoZ3LoRQ9s04sL2jQn29azDV6PuaGRARE6nkYG6U1TsICu/iIIiB/nlf4rJL3IQHuhNRLCPKblM/XV38c5jvPXzvj99HA+rBbtHSUlwOAxyCovP+KCvTESQN91bhNC9RTC9ohrQuWmQW+yNnZKSYnYEERG3ZPOwOuUvmqaWgaiGfgxr24iiYoPCYgeFxQ6KHAaFxQZFpX8vLDYoKP3/BUX/u+10xQ6DYodBXqGjwu1WCzQK8CIs0JuwQG+aBvuUzP5s7E+rRv40CnDNCYB/lmYOi4jI6VxyAqFhGP8rCaUFoaC0LNisVny9PPDztOFtt2pIXERE5A+4ZBkQERGRmqM1c0VERNycyoCIiIibUxkQERFxcyoDIiIibk5lQERExM2pDIiIiLg5lQERERE3pzIgIiLi5lQGRERE3JzKgIiIiJtTGRAREXFzKgMiIiJuTmVARETEzakMiIiIuDmVARERETenMiAiIuLmVAZERETcnK0qDzIMg4KCgtrOIiIiIrXA09MTi8Vy1vurVAYKCgp4+eWXayyUiIiI1J0nnngCLy+vs95vMQzD+KODuMrIQHJyMlOnTmXChAmEh4ebHcfp6PU5O702Z6fX5uz02pybXp+zq+vXpkZGBiwWyzkbhbPw9PQs/19XyFvX9PqcnV6bs9Nrc3Z6bc5Nr8/ZOdtrowmEIiIibq5elQF/f3+GDh2Kv7+/2VGckl6fs9Nrc3Z6bc5Or8256fU5O2d7bao0Z0BERETqr3o1MiAiIiLVpzIgIiLi5lQGRERE3JzKgIiIiJtTGRAREXFz9boMfPnll1x11VW0atWKgIAA/P396dixIw899BBJSUlmxzNNYWEhM2fOZPz48bRv3x5/f38CAgLo27cv7733HsXFxWZHNNXmzZt58sknGTlyJI0aNcJisTBs2DCzY9W5devWcemllxIcHIyfnx/9+vVj+vTpZscy3bRp07jrrrvo1asXXl5eWCwWpk6danYsp5CUlMRbb73FiBEjaNGiBZ6enoSHh3PNNdewZs0as+OZKi8vj4cffpghQ4YQERGBt7c34eHhDBw4kE8//ZTCwkJT89XrSwsvv/xy9u3bR8+ePWnSpAmGYbB582ZiY2MJCgpi+fLldOzY0eyYdW737t3lJeDCCy+kbdu2pKen8+OPP3LkyBFGjx7NnDlzzrl0ZX323HPP8fzzz+Pp6UmbNm3Yvn07Q4cOZcmSJWZHqzOxsbGMHDkSb29vrr/+egICApg5cyaJiYm89tpr/P3vfzc7ommioqJITEwkNDQUPz8/EhMT+fTTT5kwYYLZ0Uz3xBNP8Morr9CqVSuGDRtGo0aN2LdvH99//z2GYfDVV18xduxYs2OaIjU1lebNm9OnTx/atGlDo0aNOHXqFPPnzycxMZERI0Ywf/58rFaTfkc36rHc3NxKb//4448NwLj22mvrOJFzOHz4sDF58mQjKyurwu1ZWVlGr169DMCYPn26SenMt337dmPDhg1GQUGBcfToUQMwhg4danasOlNYWGi0atXK8PLyMjZt2lR+e1pamtGmTRvD09PTSEhIMC+gyRYvXlz+/b/00ksGYHz66afmhnISM2fONJYsWXLG7cuWLTPsdrsREhJi5OXlmZDMfMXFxUZ+fv4ZtxcWFhrDhg0zAOOnn34yIVmJen2awNvbu9Lbr7vuOgDi4uLqMo7TaNq0KXfffTd+fn4Vbvfz8+Phhx8GYOnSpWZEcwodO3akR48e2O12s6OY4tdffyU+Pp4bb7yRbt26ld8eFBTEk08+SUFBAZ999pl5AU120UUXERkZaXYMp3T11VczdOjQM24fPHgwF1xwAadOnWLbtm0mJDOf1Wot34/gdDabjauuugow9zOpXpeBs5k7dy4AnTp1MjmJ8yn7ALTZqrSHldRDZadDRowYccZ9I0eOBNy7LMr50XtL5RwOBwsWLADM/Uxyi/8q06dPZ+fOneTk5LBjxw4WLlxIy5Yt+de//mV2NKczZcoUoPIPAnEP+/btA6B169Zn3BceHo6/v3/5Y0Sq4uDBg/z88880adKEzp07mx3HVAUFBbz44osYhsGJEyf45Zdf2L17NxMnTuTCCy80LZfblIGZM2eW/71Xr1588803tGzZ0sRUzufDDz9k/vz5DB8+nEsvvdTsOGKS9PR0oOS0QGUCAwPLHyPyRwoLCxk3bhz5+fm88soreHh4mB3JVAUFBTz//PPlf7dYLDzyyCO89NJLJqZygTLw97//nfz8/Co//oEHHjjjN5oZM2YAkJaWxqZNm3jqqafo2bMns2bNYvjw4TWaty7VxGtT5qeffuLee+8lMjKSadOm1VRE09TkayMi58fhcDBhwgSWLVvGHXfcwbhx48yOZDp/f38Mw8DhcHDkyBF+/PFHnnzySVatWsW8efMIDAw0J5hpUxeryM/PzwCq/Cc2NvYPj5menm6Eh4cbTZs2NQoKCmr/m6glNfXazJ071/Dy8jKaNWtmxMfH1+03UUtq6rVxx6sJrr32WgMw1q9fX+n9/v7+RvPmzes4lXPS1QRnV1xcbIwfP94AjJtvvtkoLi42O5LTmj59ugEYjz32mGkZnH5kICsrq8aPGRgYSL9+/fj++++Ji4ujffv2Nf416kJNvDZz587lmmuuITQ0lNjYWKKjo2sgmflq4+fGXZSNkJSt0XG65ORksrKy6NOnjxnRxEU4HA4mTpzI559/zg033MDUqVPNu37eBZTN0TJzLRO3/a9z5MgRALe9fAz+VwQaNGhAbGwsMTExZkcSJ1B2adiiRYvOuG/hwoUVHiPye6cXgbFjx/LFF1+4/TyBP+IMn0f1tgxkZmayZ8+eSu+bMmUKa9eupXXr1m77ATh//nyuueYaQkJCiI2N1flyKXfhhRcSHR3NV199xebNm8tvT09P58UXX8TT05NbbrnFvIDitBwOB7feeiuff/451113HdOmTVMRKFV2Rdvv5eTklK/vYubE7Xq7HHFCQgLR0dH06tWLdu3a0bRpU06dOsW6devYuHEjgYGBLFiwgP79+5sdtc7t3r2bbt26kZ+fz/XXX0/btm3PeExUVJTbLq+6e/duXn75ZQByc3OZPn06YWFhjBo1qvwx9X0tei1HfHYff/wxy5cvB2Dbtm1s3LiRgQMHlv9iMWjQIG6//XYzI5qmbClvf39/HnjggUrXFBgzZkyFxazcxXPPPccbb7zBoEGDiIqKIjAwkKSkJObPn8+JEycYPHgwCxcuxMfHx5yAps1WqGVZWVnGs88+awwZMsQIDw837Ha74efnZ3Ts2NF46KGHjEOHDpkd0TSxsbF/OKHOnSbM/V5VXh93sGbNGmPUqFFGYGCg4ePjY/Tp08f45ptvzI5lurJJcWf7M378eLMjmuaPXhvceLLlunXrjDvuuMPo2LGjERwcbNhsNqNhw4bGBRdcYHzwwQdGYWGhqfnq7ciAiIiIVE29nTMgIiIiVaMyICIi4uZUBkRERNycyoCIiIibUxkQERFxcyoDIiIibk5lQERExM2pDIiIiLg5lQERERE3pzIgIiLi5lQGRERE3JzKgIiIiJv7f1Fmj5aVWU6JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idata = az.from_cmdstanpy(fit)\n",
    "az.plot_posterior(idata);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pymc",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  },
  "name": "PyStan_test_installation.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
